Data Integrity

Data integrity refers to whether data stays accurate, complete, and unaltered across its lifecycle, from collection and labeling through training, storage, and use. In AI governance, it matters because a model is only as trustworthy as the data it learns from, and corrupted, mislabeled, or tampered records can produce biased or unreliable outputs that are hard to detect after deployment. Compliance teams typically need documented evidence of where training and operational data came from, who changed it, and how its accuracy was checked, since regulators and auditors increasingly ask for that proof.

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ResearchGlobal2026-11-12

25 Autonomous AI Intrusions Hit 19 Orgs Using Cairn, Zhipu GLM, and DeepSeek

Palo Alto Networks Unit 42 documented 25 autonomous AI-driven intrusion operations against 19 organizations, observed in August and September 2026. Attackers used an open-source penetration-testing framework called Cairn, paired with Chinese AI models Zhipu GLM 5.2 and DeepSeek V4. They breached 10 organizations, stole credentials and records, created unauthorized administrator accounts, and overwrote a live industry-standards ballot. The campaign was attributed to Chinese-speaking operators who may have run all 25 operations with minimal staffing, hitting 19 organizations over roughly two weeks.

AI-enabled cyberattack autonomous attack agents Zhipu GLM DeepSeek threat intelligence agentic AI enterprise risk data integrity